2,377 research outputs found
Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation
Constrained Concept Factorization (CCF) yields the enhanced representation
ability over CF by incorporating label information as additional constraints,
but it cannot classify and group unlabeled data appropriately. Minimizing the
difference between the original data and its reconstruction directly can enable
CCF to model a small noisy perturbation, but is not robust to gross sparse
errors. Besides, CCF cannot preserve the manifold structures in new
representation space explicitly, especially in an adaptive manner. In this
paper, we propose a joint label prediction based Robust Semi-Supervised
Adaptive Concept Factorization (RS2ACF) framework. To obtain robust
representation, RS2ACF relaxes the factorization to make it simultaneously
stable to small entrywise noise and robust to sparse errors. To enrich prior
knowledge to enhance the discrimination, RS2ACF clearly uses class information
of labeled data and more importantly propagates it to unlabeled data by jointly
learning an explicit label indicator for unlabeled data. By the label
indicator, RS2ACF can ensure the unlabeled data of the same predicted label to
be mapped into the same class in feature space. Besides, RS2ACF incorporates
the joint neighborhood reconstruction error over the new representations and
predicted labels of both labeled and unlabeled data, so the manifold structures
can be preserved explicitly and adaptively in the representation space and
label space at the same time. Owing to the adaptive manner, the tricky process
of determining the neighborhood size or kernel width can be avoided. Extensive
results on public databases verify that our RS2ACF can deliver state-of-the-art
data representation, compared with other related methods.Comment: Accepted at IEEE TKD
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the
"shallow" parts-based representation, but it clearly fails to discover deep
hierarchical information within both the basis and representation spaces. In
this paper, we technically propose a new enriched prior based Dual-constrained
Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net, for
learning the hierarchical coupled representations. To ex-tract hidden deep
features, DS2CF-Net is modeled as a deep-structure and geometrical
structure-constrained neural network. Specifically, DS2CF-Net designs a deep
coupled factorization architecture using multi-layers of linear
transformations, which coupled updates the bases and new representations in
each layer. To improve the discriminating ability of learned deep
representations and deep coefficients, our network clearly considers enriching
the supervised prior by the joint deep coefficients-regularized label
prediction, and incorporates enriched prior information as additional label and
structure constraints. The label constraint can enable the samples of the same
label to have the same coordinate in the new feature space, while the structure
constraint forces the coefficient matrices in each layer to be block-diagonal
so that the enhanced prior using the self-expressive label propagation are more
accurate. Our network also integrates the adaptive dual-graph learning to
retain the local manifold structures of both the data manifold and feature
manifold by minimizing the reconstruction errors in each layer. Extensive
experiments on several real databases demonstrate that our DS2CF-Net can obtain
state-of-the-art performance for representation learning and clustering
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
We investigate the high-dimensional data clustering problem by proposing a
novel and unsupervised representation learning model called Robust Flexible
Auto-weighted Local-coordinate Concept Factorization (RFA-LCF). RFA-LCF
integrates the robust flexible CF, robust sparse local-coordinate coding and
the adaptive reconstruction weighting learning into a unified model. The
adaptive weighting is driven by including the joint manifold preserving
constraints on the recovered clean data, basis concepts and new representation.
Specifically, our RFA-LCF uses a L2,1-norm based flexible residue to encode the
mismatch between clean data and its reconstruction, and also applies the robust
adaptive sparse local-coordinate coding to represent the data using a few
nearby basis concepts, which can make the factorization more accurate and
robust to noise. The robust flexible factorization is also performed in the
recovered clean data space for enhancing representations. RFA-LCF also
considers preserving the local manifold structures of clean data space, basis
concept space and the new coordinate space jointly in an adaptive manner way.
Extensive comparisons show that RFA-LCF can deliver enhanced clustering
results.Comment: Accepted at the 44th IEEE International Conference on Acoustics,
Speech, and Signal Processing(ICASSP 2019
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning
issue and technically propose a novel framework called Deep Self-representative
Concept Factorization Network (DSCF-Net), for clustering deep features. To
improve the representation and clustering abilities, DSCF-Net explicitly
considers discovering hidden deep semantic features, enhancing the robustness
proper-ties of the deep factorization to noise and preserving the local
man-ifold structures of deep features. Specifically, DSCF-Net seamlessly
integrates the robust deep concept factorization, deep self-expressive
representation and adaptive locality preserving feature learning into a unified
framework. To discover hidden deep repre-sentations, DSCF-Net designs a
hierarchical factorization architec-ture using multiple layers of linear
transformations, where the hierarchical representation is performed by
formulating the prob-lem as optimizing the basis concepts in each layer to
improve the representation indirectly. DSCF-Net also improves the robustness by
subspace recovery for sparse error correction firstly and then performs the
deep factorization in the recovered visual subspace. To obtain
locality-preserving representations, we also present an adaptive deep
self-representative weighting strategy by using the coefficient matrix as the
adaptive reconstruction weights to keep the locality of representations.
Extensive comparison results with several other related models show that
DSCF-Net delivers state-of-the-art performance on several public databases.Comment: Accepted by SDM 202
GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models
Modern data analysis pipelines are becoming increasingly complex due to the
presence of multi-view information sources. While graphs are effective in
modeling complex relationships, in many scenarios a single graph is rarely
sufficient to succinctly represent all interactions, and hence multi-layered
graphs have become popular. Though this leads to richer representations,
extending solutions from the single-graph case is not straightforward.
Consequently, there is a strong need for novel solutions to solve classical
problems, such as node classification, in the multi-layered case. In this
paper, we consider the problem of semi-supervised learning with multi-layered
graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for
community discovery, we argue that feature learning with random node
attributes, using graph neural networks, can be more effective. To this end, we
propose to use attention models for effective feature learning, and develop two
novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer
dependencies for building multi-layered graph embeddings. Using empirical
studies on several benchmark datasets, we evaluate the proposed approaches and
demonstrate significant performance improvements in comparison to
state-of-the-art network embedding strategies. The results also show that using
simple random features is an effective choice, even in cases where explicit
node attributes are not available
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
Information Extraction from Scientific Literature for Method Recommendation
As a research community grows, more and more papers are published each year.
As a result there is increasing demand for improved methods for finding
relevant papers, automatically understanding the key ideas and recommending
potential methods for a target problem. Despite advances in search engines, it
is still hard to identify new technologies according to a researcher's need.
Due to the large variety of domains and extremely limited annotated resources,
there has been relatively little work on leveraging natural language processing
in scientific recommendation. In this proposal, we aim at making scientific
recommendations by extracting scientific terms from a large collection of
scientific papers and organizing the terms into a knowledge graph. In
preliminary work, we trained a scientific term extractor using a small amount
of annotated data and obtained state-of-the-art performance by leveraging large
amount of unannotated papers through applying multiple semi-supervised
approaches. We propose to construct a knowledge graph in a way that can make
minimal use of hand annotated data, using only the extracted terms,
unsupervised relational signals such as co-occurrence, and structural external
resources such as Wikipedia. Latent relations between scientific terms can be
learned from the graph. Recommendations will be made through graph inference
for both observed and unobserved relational pairs.Comment: Thesis Proposal. arXiv admin note: text overlap with arXiv:1708.0607
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
Concept Factorization (CF) and its variants may produce inaccurate
representation and clustering results due to the sensitivity to noise, hard
constraint on the reconstruction error and pre-obtained approximate
similarities. To improve the representation ability, a novel unsupervised
Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF)
framework is proposed for clustering high-dimensional data. Specifically,
RFA-LCF integrates the robust flexible CF by clean data space recovery, robust
sparse local-coordinate coding and adaptive weighting into a unified model.
RFA-LCF improves the representations by enhancing the robustness of CF to noise
and errors, providing a flexible constraint on the reconstruction error and
optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a
sparse projection to recover the underlying clean data space, and then the
flexible CF is performed in the projected feature space. RFA-LCF also uses a
L2,1-norm based flexible residue to encode the mismatch between the recovered
data and its reconstruction, and uses the robust sparse local-coordinate coding
to represent data using a few nearby basis concepts. For auto-weighting,
RFA-LCF jointly preserves the manifold structures in the basis concept space
and new coordinate space in an adaptive manner by minimizing the reconstruction
errors on clean data, anchor points and coordinates. By updating the
local-coordinate preserving data, basis concepts and new coordinates
alternately, the representation abilities can be potentially improved.
Extensive results on public databases show that RFA-LCF delivers the
state-of-the-art clustering results compared with other related methods.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (IEEE
TKDE
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging
from biology and communications to ocean and Earth science. We survey the
recent advances and transformative potential of machine learning (ML),
including deep learning, in the field of acoustics. ML is a broad family of
techniques, which are often based in statistics, for automatically detecting
and utilizing patterns in data. Relative to conventional acoustics and signal
processing, ML is data-driven. Given sufficient training data, ML can discover
complex relationships between features and desired labels or actions, or
between features themselves. With large volumes of training data, ML can
discover models describing complex acoustic phenomena such as human speech and
reverberation. ML in acoustics is rapidly developing with compelling results
and significant future promise. We first introduce ML, then highlight ML
developments in four acoustics research areas: source localization in speech
processing, source localization in ocean acoustics, bioacoustics, and
environmental sounds in everyday scenes.Comment: Published with free access in Journal of the Acoustical Society of
America, 27 Nov. 201
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